Abstract

Advances in photo editing software have made it possible togenerate visually convincing photographic forgeries which have beenincreased tremendously in recent years. In order to alleviate the problem ofimage forgery, a handful of techniques have been presented in the literatureto detect forgery either in shadow or reflection. This paper aims to developa technique to detect the image forgery either in shadow or reflection usingfeatures enabled neural network. The proposed technique of image forgerydetection contains three important steps, like segmentation, featureextraction and detection. In segmentation, shadow points and reflectionpoints are identified using map-based segmentation and FCM clustering. Then,feature points from the shadow points and reflective parts are extracted byconsidering texture consistency and strength consistency using LVP operator.The final step of forgery detection is performed using the feed forwardneural network, where a new algorithm called ABCLM is developed for trainingof neural network weights. The performance is analyzed with four existingalgorithms using measures such as accuracy and MSE. From the analysis, weunderstand that the proposed technique obtained the maximum accuracy of80.49%